Reinforcement Learning Engineer

Bugcrowd

. Design, build, and deploy high-throughput RLVR environments evaluating LLM action sequences against deterministic execution outcomes .

Posted 10/26/2026full-timeRemote • United StatesMid-LevelSenior💰 $176,400 - $242,550 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in Reinforcement Learning workflows, particularly in developing and deploying high-throughput RLVR environments. Proficient in Python and C programming, with a solid understanding of software vulnerabilities and experience in building benchmark environments for AI safety and reasoning models.

Highest-signal resume keywords

Reinforcement Learning From Verifiable Rewards (RLVR)Python Application DevelopmentLow-Level Systems Programming In CDocker And BuildKitDevOps Pipelines

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Reinforcement LearningAutomated Test Harness DevelopmentBinary ExploitationFuzzing MethodologiesTelemetry ImplementationLow-Level DebuggingBenchmark Environment DesignCustom Reward FunctionsAutomated Grading EnginesMemory State Instrumentation

Soft Skills

IntegrityCollaboration

Tools & Technologies

DockerBuildKitNixLinux SystemsBugcrowd MayhemGitHub Actions

Industry Keywords

Software VulnerabilitiesProgram AnalysisCybersecurity BenchmarksCTFsAutomated Exploit Generation

Tech Stack

Tools & technologies

Cyber SecurityDockerLinuxPythonRust

About the role

Key responsibilities & impact

Design, build, and deploy high-throughput RLVR environments evaluating LLM action sequences against deterministic execution outcomes
Develop automated test harnesses, sandboxes, and verification engines that convert vulnerability research into binary pass/fail reward signals
Integrate Bugcrowd’s Mayhem automated analysis platform and real-world vulnerability feeds into scalable RL environment generation pipelines
Architect safe, isolated, reproducible execution environments using Docker, BuildKit, or Nix
Collaborate with researchers at Anthropic, OpenAI, and Cohere to define benchmark formats, observation spaces, and verifiable evaluation metrics
Implement telemetry, ground-truth verification algorithms, and trajectory logging to analyze agent reasoning and prevent reward hacking or false positives
Build low-level instrumentation and debugging tools for memory states, process executions, and network behaviors
Optimize infrastructure performance and environment reset latency for large-scale parallel sampling and distributed RL training
Benchmark frontier AI models across offensive and defensive security challenges including automated fuzzing, exploit payload generation, and patch validation

Requirements

What you’ll need

Understanding of RL training workflows used by modern LLM systems, specifically execution-based feedback or Reinforcement Learning from Verifiable Rewards (RLVR)
Proficiency developing applications in Python
Proficiency in low-level systems programming in C
Rust experience is a strong plus
Solid understanding of software vulnerabilities, binary exploitation, fuzzing methodologies, or program analysis
Experience with DevOps pipelines such as GitHub Actions
Experience with reproducible builds using Docker, BuildKit, or Nix
Comfort working with Linux systems and low-level debugging
Experience working with or building benchmark environments such as CTFs, SWE-bench, security challenges, or execution sandboxes
Preferred: experience designing custom reward functions, ground-truth verifiers, or automated grading engines for AI safety and reasoning models
Preferred: background in low-level program analysis tools, sanitizers such as ASan/MSan, compiler instrumentation, or automated exploit generation tools
Preferred: track record of participating in or developing competitive cybersecurity benchmarks, CTFs, or open-source AI evaluation frameworks
Ability to complete all physical requirements with or without reasonable accommodation
Ability to remain in a stationary position 50% of the time
Ability to carry or move a laptop as needed throughout the work day
Integrity to maintain confidential information in the strictest confidence
Successful completion of applicable background checks

Benefits

Comp & perks

Discretionary bonus program or commission plan eligibility
Reasonable accommodation for disabilities
Work-from-home arrangement